Systems and methods for estimating blood flow characteristics from vessel geometry and physiology

ABSTRACT

Systems and methods are disclosed for estimating patient-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, a geometric model and estimated blood flow characteristics of at least part of the individual&#39;s vascular system; executing a machine learning algorithm on the geometric model and estimated blood flow characteristics for each of the plurality of individuals; identifying, using the machine learning algorithm, features predictive of blood flow characteristics corresponding to a plurality of points in the geometric models; acquiring, for a patient, a geometric model of at least part of the patient&#39;s vascular system; and using the identified features to produce estimates of the patient&#39;s blood flow characteristic for each of a plurality of points in the patient&#39;s geometric model.

RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/700,213 filed Sep. 12, 2012, the entire disclosure of which is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

Various embodiments of the present disclosure relate generally tomedical imaging and related methods. More specifically, particularembodiments of the present disclosure relate to systems and methods forestimating patient-specific blood flow characteristics from vesselgeometry and physiology.

BACKGROUND

A functional assessment of arterial capacity is important for treatmentplanning to address patient needs. Recent studies have demonstrated thathemodynamic characteristics, such as Fractional Flow Reserve (FFR), areimportant indicators to determine the optimal treatment for a patientwith arterial disease. Conventional assessments of these hemodynamiccharacteristics use invasive catheterizations to directly measure bloodflow characteristics, such as pressure and flow velocity. However,despite the important clinical information that is gathered, theseinvasive measurement techniques present severe risks to the patient andsignificant costs to the healthcare system.

To address the risks and costs associated with invasive measurement, anew generation of noninvasive tests have been developed to assess bloodflow characteristics. These noninvasive tests use patient imaging (suchas computed tomography (CT)) to determine a patient-specific geometricmodel of the blood vessels and this model is used computationally tosimulate the blood flow using computational fluid dynamics (CFD) withappropriate physiological boundary conditions and parameters. Examplesof inputs to these patient-specific boundary conditions include thepatient's blood pressure, blood viscosity and the expected demand ofblood from the supplied tissue (derived from scaling laws and a massestimation of the supplied tissue from the patient imaging). Althoughthese simulation-based estimations of blood flow characteristics havedemonstrated a level of fidelity comparable to direct (invasive)measurements of the same quantity of interest, physical simulationsdemand a substantial computational burden that can make these virtual,noninvasive tests difficult to execute in a real-time clinicalenvironment. Consequently, the present disclosure describes newapproaches for performing rapid, noninvasive estimations of blood flowcharacteristics that are computationally inexpensive.

SUMMARY

Systems and methods are disclosed for deriving a patient-specificgeometric model of a patient's blood vessels, and combining thisgeometry with the patient-specific physiological information andboundary conditions. Combined, these data may be used to estimate thepatient's blood flow characteristics and predict clinically relevantquantities of interest (e.g., FFR). The presently disclosed systems andmethods offer advantages over physics-based simulation of blood flow tocompute the quantity of interest, such as by instead using machinelearning to predict the results of a physics-based simulation. In oneembodiment, disclosed systems and methods involve two phases: first, atraining phase in which a machine learning system is trained to predictone or more blood flow characteristics; and second, a production phasein which the machine learning system is used to produce one or moreblood flow characteristics and clinically relevant quantities ofinterest. In the case of predicting multiple blood flow characteristics,this machine learning system can be applied for each blood flowcharacteristic and quantity of interest.

According to one embodiment, a method is disclosed for estimatingpatient-specific blood flow characteristics. The method includesacquiring, for each of a plurality of individuals, a geometric model andestimated blood flow characteristics of at least part of theindividual's vascular system; executing a machine learning algorithm onthe geometric model and estimated blood flow characteristics for each ofthe plurality of individuals; identifying, using the machine learningalgorithm, features predictive of blood flow characteristicscorresponding to a plurality of points in the geometric models;acquiring, for a patient, a geometric model of at least part of thepatient's vascular system; and using the identified features to produceestimates of the patient's blood flow characteristic for each of aplurality of points in the patient's geometric model.

According to another embodiment, a system is disclosed for estimatingpatient-specific blood flow characteristics. The system includes a datastorage device storing instructions for estimating patient-specificblood flow characteristics; and a processor configured to execute theinstructions to perform a method including the steps of: acquiring, foreach of a plurality of individuals, a geometric model and estimatedblood flow characteristics of at least part of the individual's vascularsystem; executing a machine learning algorithm on the geometric modeland estimated blood flow characteristics for each of the plurality ofindividuals; identifying, using the machine learning algorithm, featurespredictive of blood flow characteristics corresponding to a plurality ofpoints in the geometric models; acquiring, for a patient, a geometricmodel of at least part of the patient's vascular system; and using theidentified features to produce estimates of the patient's blood flowcharacteristic for each of a plurality of points in the patient'sgeometric model.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forestimating patient-specific blood flow characteristics from vesselgeometry and physiological information, according to an exemplaryembodiment of the present disclosure.

FIG. 2 is a block diagram of an exemplary method for estimatingpatient-specific blood flow characteristics from vessel geometry andphysiological information, according to an exemplary embodiment of thepresent disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

The present disclosure describes certain principles and embodiments forproviding advantages over physics-based simulation of blood flow tocompute patient-specific blood flow characteristics and clinicallyrelevant quantities of interest. Namely, the presently disclosed systemsand methods may incorporate machine learning techniques to predict theresults of a physics-based simulation. For example, the presentdisclosure describes an exemplary, less processing-intensive technique,which may involve modeling the fractional flow reserve (FFR) as afunction of a patient's vascular cross-sectional area, diseased length,and boundary conditions. The cross-sectional area may be calculatedbased on lumen segment and plaque segment, among other things. Thediseased length may be calculated based on plaque segment and stenosislocation, among other things. The boundary conditions may reflectpatient-specific physiology, such as coronary flow (estimated frommyocardial mass), outlet area, and hyperemic assumptions, to reflectthat different patients have different geometry and physiologicresponses.

In one embodiment, fractional flow reserve may be modeled as a functionof a patient's boundary conditions (f(BCs)), and a function of apatient's vascular geometry (g(areaReductions)). Although the patient'sgeometry may be described as a function of “areaReductions,” it shouldbe appreciated that this term refers, not just to changes in patient'svascular cross-sectional area, but to any physical or geometriccharacteristics affecting a patient's blood flow. In one embodiment, FFRcan be predicted by optimizing the functions “f” and “g” such that thedifference between the estimated FFR (FFR_(CT_ScalingLaw)) and themeasured FFR (mFFR) is minimized. In other words, machine learningtechniques can be used to solve for the functions that cause theestimated FFR to approximate the measured FFR. In one embodiment, themeasured FFR may be calculated by traditional catheterized methods or bymodern, computational fluid dynamics (CFD) techniques. In oneembodiment, one or more machine learning algorithms may be used tooptimize the functions of boundary conditions and patient geometry forhundreds or even thousands of patients, such that estimates for FFR canreliably approximate measured FFR values. Thus, FFR values calculated byCFD techniques can be valuable for training the machine learningalgorithms.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system and network for estimating patient-specific blood flowcharacteristics from vessel geometry and physiological information.Specifically, FIG. 1 depicts a plurality of physicians 102 and thirdparty providers 104, any of whom may be connected to an electronicnetwork 100, such as the Internet, through one or more computers,servers, and/or handheld mobile devices. Physicians 102 and/or thirdparty providers 104 may create or otherwise obtain images of one or morepatients' cardiac and/or vascular systems. The physicians 102 and/orthird party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, etc. Physicians 102 and/or third partyproviders 104 may transmit the cardiac/vascular images and/orpatient-specific information to server systems 106 over the electronicnetwork 100. Server systems 106 may include storage devices for storingimages and data received from physicians 102 and/or third partyproviders 104. Sever systems 106 may also include processing devices forprocessing images and data stored in the storage devices.

FIG. 2 is a block diagram of an exemplary method for estimatingpatient-specific blood flow characteristics from vessel geometry andphysiological information, according to an exemplary embodiment of thepresent disclosure. The method of FIG. 2 may be performed by serversystems 106, based on information received from physicians 102 and/orthird party providers 104 over electronic network 100. [018] in oneembodiment, the method of FIG. 2 may include a training method 202, fortraining one or more machine learning algorithms based on numerouspatients' blood flow characteristic estimates, and a production method204 for using the machine learning algorithm results to predict aparticular patient's blood flow characteristics.

In one embodiment, training method 202 may be performed based on FFRestimates generating using CFD techniques for hundreds of patients.Training method 202 may involve acquiring, for each of a plurality ofindividuals, e.g., in digital format: (a) a patient-specific geometricmodel, (b) one or more measured or estimated physiological parameters,and (c) values of blood flow characteristics. Training method 202 maythen involve, for one or more points in each patient's model, creating afeature vector of the patients' physiological parameters and associatingthe feature vector with the values of blood flow characteristics. Forexample, training method 202 may associate an estimated FFR with everypoint in a patient's geometric model. Training method 202 may then traina machine learning algorithm (e.g., using processing devices of serversystems 106) to predict blood flow characteristics at each point of ageometric model, based on the feature vectors and blood flowcharacteristics. Training method 202 may then save the results of themachine learning algorithm, including feature weights, in a storagedevice of server systems 106. The stored feature weights may define theextent to which patient features or geometry are predictive of certainblood flow characteristics.

In one embodiment, the production method 204 may involve estimating FFRvalues for a particular patient, based on results of executing trainingmethod 202. In one embodiment, production method 204 may includeacquiring, e.g. in digital format: (a) a patient-specific geometricmodel, and (b) one or more measured or estimated physiologicalparameters. For multiple points in the patient's geometric model,production method 204 may involve creating a feature vector of thephysiological parameters used in the training mode. Production method204 may then use saved results of the machine learning algorithm toproduce estimates of the patient's blood flow characteristics for eachpoint in the patient-specific geometric model. Finally, productionmethod 204 may include saving the results of the machine learningalgorithm, including predicted blood flow characteristics, to a storagedevice of server systems 106.

Described below are general and specific exemplary embodiments forimplementing a training mode and a production mode of machine learningfor predicting patient-specific blood flow characteristics, e.g. usingserver systems 106 based on images and data received from physicians 102and/or third party providers 104 over electronic network 100.

General Embodiment

In a general embodiment, server systems 106 may perform a training modebased on images and data received from physicians 102 and/or third partyproviders 104 over electronic network 100. Specifically, for one or morepatients, server systems 106 may acquire a digital representation (e.g.,the memory or digital storage [e.g., hard drive, network drive] of acomputational device such as a computer, laptop, DSP, server, etc.) ofthe following items: (a) a patient-specific model of the geometry forone or more of the patient's blood vessels; (b) a list of one or moremeasured or estimated physiological or phenotypic parameters of thepatient; and/or (c) measurements, estimations or simulated values of allblood flow characteristic being targeted for prediction. In oneembodiment, the patient-specific model of the geometry may berepresented by a list of points in space (possibly with a list ofneighbors for each point) in which the space can be mapped to spatialunits between points (e.g., millimeters). In one embodiment, the list ofone or more measured or estimated physiological or phenotypic parametersof the patient may include blood pressure, blood viscosity, patient age,patient gender, mass of the supplied tissue, etc. These patient-specificparameters may be global (e.g., blood pressure) or local (e.g.,estimated density of the vessel wall at a particular location).

For every point in the patient-specific geometric model for which thereis a measured, estimated or simulated value of the blood flowcharacteristic, server systems 106 may then create a feature vector forthat point. The feature vector may be a numerical description of thepatient-specific geometry at that point and estimates of physiologicalor phenotypic parameters of the patient. The feature vector may containboth global and local physiological or phenotypic parameters, where: forglobal parameters, all points have the same numerical value; and forlocal parameters, the value(s) may change at different points in thefeature vector. Server systems 106 may then associate this featurevector with the measured, estimated or simulated value of the blood flowcharacteristic at this point.

Server systems 106 may then train a machine learning algorithm topredict the blood flow characteristics at the points from the featurevectors at the points. Examples of machine learning algorithms that canperform this task are support vector machines (SVMs), multi-layerperceptrons (MLPs), and multivariate regression (MVR) (e.g., weightedlinear or logistic regression). Server systems 106 may then save theresults of the machine learning algorithm (e.g., feature weights) to adigital representation (e.g., the memory or digital storage [e.g., harddrive, network drive] of a computational device such as a computer,laptop, DSP, server, etc.).

Also in a general embodiment, server systems 106 may perform aproduction mode based on images and data received from physicians 102and/or third party providers 104 over electronic network 100. For apatient on whom a blood flow analysis is to be performed, server systems106 may acquire a digital representation (e.g., the memory or digitalstorage [e.g., hard drive, network drive] of a computational device suchas a computer, laptop, DSP, server, etc.) of (a) a patient-specificmodel of the geometry for one or more of the patient's blood vessels;and (b) a list of one or more estimates of physiological or phenotypicparameters of the patient. In one embodiment, the patient-specific modelof the geometry for one or more of the patient's blood vessels may berepresented as a list of points in space (possibly with a list ofneighbors for each point) in which the space can be mapped to spatialunits between points (e.g., millimeters). The list of one or moreestimates of physiological or phenotypic parameters of the patient, mayinclude blood pressure, blood viscosity, patient age, patient gender,the mass of the supplied tissue, etc. These parameters may be global(e.g., blood pressure) or local (e.g., estimated density of the vesselwall at a location). This list of parameters must be the same as thelist used in the training mode.

For every point in the patient-specific geometric model, server systems106 may create a feature vector that consists of a numerical descriptionof the geometry and estimates of physiological or phenotypic parametersof the patient. Global physiological or phenotypic parameters can beused in the feature vector of all points and local physiological orphenotypic parameters can change in the feature vector of differentpoints. These feature vectors may represent the same parameters used inthe training mode. Server systems 106 may then use the saved results ofthe machine learning algorithm produced in the training mode (e.g.,feature weights) to produce estimates of the blood flow characteristicsat each point in the patient-specific geometric model. These estimatesmay be produced using the same machine learning algorithm technique usedin the training mode (e.g., the SVM, MLP, MVR technique). Server systems106 may also save the predicted blood flow characteristics for eachpoint to a digital representation (e.g., the memory or digital storage[e.g., hard drive, network drive] of a computational device such as acomputer, laptop, DSP, server, etc.).

Exemplary Embodiment

In one exemplary embodiment, server systems 106 may perform a trainingmode based on images and data received from physicians 102 and/or thirdparty providers 104 over electronic network 100. Specifically, for oneor more patients, server systems 106 may acquire a digitalrepresentation (e.g., the memory or digital storage [e.g., hard drive,network drive] of a computational device such as a computer, laptop,DSP, server, etc.) of (a) a patient-specific model of the geometry forthe patient's ascending aorta and coronary artery tree; (b) a list ofmeasured or estimated physiological or phenotypic parameters of thepatient; and (c) measurements of the FFR when available.

In one embodiment, the patient-specific model of the geometry for thepatient's ascending aorta and coronary artery tree may be represented asa list of points in space (possibly with a list of neighbors for eachpoint) in which the space can be mapped to spatial units between points(e.g., millimeters). This model may be derived by performing a cardiacCT imaging study of the patient during the end diastole phase of thecardiac cycle. The resulting CT images may then be segmented manually orautomatically to identify voxels belonging to the aorta and to the lumenof the coronary arteries. Once all relevant voxels are identified, thegeometric model can be derived (e.g., using marching cubes).

In one embodiment, the list of measured or estimated physiological orphenotypic parameters of the patient may be obtained and may include:(i) systolic and diastolic blood pressures; (ii) heart rate; (iii)hematocrit level; (iv) patient age, gender, height, weight, generalhealth status (presence or absence of diabetes, current medications);(v) lifestyle characteristics: smoker/non-smoker; and/or (vi) myocardialmass (may be derived by segmenting the myocardium obtained during the CTimaging study and then calculating the volume in the image; the mass isthen computed using the computed volume and an estimated density (1.05g/mL) of the myocardial mass.

In one embodiment, measurements of the FFR may be obtained whenavailable. If the measured FFR value is not available at a given spatiallocation in the patient-specific geometric model, then a numericallycomputed value of the FFR at the point may be used. The numericallycomputed values may be obtained from a previous CFD simulation using thesame geometric model and patient-specific boundary conditions derivedfrom the physiological and phenotypic parameters listed above.

For every point in the patient-specific geometric model for which thereis a measured, estimated or simulated value of the blood flowcharacteristics, server systems 106 may create a feature vector for thatpoint that contains a numerical description of physiological orphenotypic parameters of the patient and a description of the localgeometry. Specifically the feature vector may contain: (i) systolic anddiastolic blood pressures; (ii) heart rate; (iii) blood propertiesincluding: plasma, red blood cells (erythrocytes), hematocrit, whiteblood cells (leukocytes) and platelets (thrombocytes), viscosity, yieldstress; (iv) patient age, gender, height, weight, etc.; (v) diseases:presence or absence of diabetes, myocardial infarction, malignant andrheumatic conditions, peripheral vascular conditions, etc.; (vi)lifestyle characteristics: presence or absence of currentmedications/drugs, smoker/non-smoker; (vii) characteristics of theaortic geometry (Cross-sectional area of the aortic inlet and outlet,Surface area and volume of the aorta, Minimum, maximum, and averagecross-sectional area, etc.); (viii) characteristics of the coronarybranch geometry; and (ix) one or more feature sets.

In one embodiment, the characteristics of the coronary branch geometrymay include: (i) volumes of the aorta upstream/downstream of thecoronary branch point; (ii) cross-sectional area of the coronary/aortabifurcation point, i.e., inlet to the coronary branch; (iii) totalnumber of vessel bifurcations, and the number of upstream/downstreamvessel bifurcations; (iv) average, minimum, and maximumupstream/downstream cross-sectional areas; (v) distances (along thevessel centerline) to the centerline point of minimum and maximumupstream/downstream cross-sectional areas; (vi) cross-sectional of anddistance (along the vessel centerline) to the nearestupstream/downstream vessel bifurcation; (vii) cross-sectional area ofand distance (along the vessel centerline) to the nearest coronaryoutlet and aortic inlet/outlet; (viii) cross-sectional areas anddistances (along the vessel centerline) to the downstream coronaryoutlets with the smallest/largest cross-sectional areas; (ix)upstream/downstream volumes of the coronary vessels; and (x)upstream/downstream volume fractions of the coronary vessel with across-sectional area below a user-specified tolerance.

In one embodiment, a first feature set may define cross-sectional areafeatures, such as a cross-sectional lumen area along the coronarycenterline, a powered cross-sectional lumen area, a ratio of lumencross-sectional area with respect to the main ostia (LM, RCA), a poweredratio of lumen cross-sectional area with respect to the main ostia, adegree of tapering in cross-sectional lumen area along the centerline,locations of stenotic lesions, lengths of stenotic lesions, location andnumber of lesions corresponding to 50%, 75%, 90% area reduction,distance from stenotic lesion to the main ostia, and/or irregularity (orcircularity) of cross-sectional lumen boundary.

In one embodiment, the cross-sectional lumen area along the coronarycenterline may be calculated by extracting a centerline from constructedgeometry, smoothing the centerline if necessary, and computingcross-sectional area at each centerline point and map it tocorresponding surface and volume mesh points. In one embodiment, thepowered cross-sectional lumen area can be determined from various sourceof scaling laws. In one embodiment, the ratio of lumen cross-sectionalarea with respect to the main ostia (LM, RCA) can be calculated bymeasuring cross-sectional area at the LM ostium, normalizingcross-sectional area of the left coronary by LM ostium area, measuringcross-sectional area at the RCA ostium, and normalizing cross-sectionalarea of the right coronary by RCA ostium area. In one embodiment, thepowered ratio of lumen cross-sectional area with respect to the mainostia can be determined from various source of scaling laws. In oneembodiment, the degree of tapering in cross-sectional lumen area alongthe centerline can be calculated by sampling centerline points within acertain interval (e.g., twice the diameter of the vessel) and compute aslope of linearly-fitted cross-sectional area. In one embodiment, thelocation of stenotic lesions can be calculated by detecting minima ofcross-sectional area curve, detecting locations where first derivativeof area curve is zero and second derivative is positive, and computingdistance (parametric arc length of centerline) from the main ostium. Inone embodiment, the lengths of stenotic lesions can be calculated bycomputing the proximal and distal locations from the stenotic lesion,where cross-sectional area is recovered.

In one embodiment, another feature set may include intensity featuresthat define, for example, intensity change along the centerline (slopeof linearly-fitted intensity variation). In one embodiment, anotherfeature set may include surface features that define, for example, 3Dsurface curvature of geometry (Gaussian, maximum, minimum, mean). In oneembodiment, another feature set may include volume features that define,for example, a ratio of total coronary volume compared to myocardialvolume. In one embodiment, another feature set may include centerlinefeatures that define, for example, curvature (bending) of coronarycenterline, e.g., by computing Frenet curvature

${{= \frac{{\text{?} \times \text{?}\text{?}}}{{\text{?}^{\text{?}}}^{\text{?}}}},{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{346mu}$

where p is coordinate of centerline

or by computing an inverse of the radius of circumscribed circle alongthe centerline points. Curvature (bending) of coronary centerline mayalso be calculated based on tortuosity (non-planarity) of coronarycenterline, e.g., by computing Frenet torsion:

${{\text{?} = \frac{( {\text{?} \times  \text{?} \sim} ) \text{?} \sim}{{\text{?} \times  \text{?} \sim}}},{\text{?}\text{indicates text missing or illegible when filed}}}\mspace{346mu}$

where p is coordinate of centerline

In one embodiment, another feature set may include a SYNTAX scoringfeature, including, for example, an existence of aorto ostial lesion,detection of a lesion located at the origin of the coronary from theaorta; and/or dominance (left or right).

In one embodiment, another feature set may include a simplified physicsfeature, e.g., including a fractional flow reserve value derived fromHagen-Poisseille flow assumption (˜

). For example, in one embodiment, server systems 106 may compute thecross-sectional area of the origin (LM ostium or RCA ostium) of thecoronary from the aorta ( ) with aortic pressure (

); compute cross-sectional area of coronary vessel (

) at each sampled interval (

); determine the amount of coronary flow in each segment of vessel usingresistance boundary condition under hyperemic assumption (

); estimate resistance at each sampled location (

based on:

$\text{?} = {{\text{?}\frac{\text{?}\text{?}\text{?}}{\text{?}\text{?}}} + \text{?}_{\text{?}}}$?indicates text missing or illegible when filed                    

where:

Nominal value=dynamic viscosiy of blood,

=1.0,

=0,

=2.0 (Hagen

Poisseille).

Server systems 106 may estimate pressure drop (

)

=

and compute FFR at each sampled location as

${{FFR}_{\text{?}} = {{\frac{\text{?}\text{?}\text{?}\text{?}\text{?}}{\text{?}}.\text{?}}\text{indicates text missing or illegible when filed}}}\mspace{346mu}$

Locations of cross-sectional area minima or intervals smaller thanvessel radius may be used for sampling locations. Server systems 106 mayinterpolate FFR along the centerline using FFR

project FFR values to 3D surface mesh node, and vary

and obtain new sets of FFR estimation as necessary for training, such asby using the feature sets defined above to perturb parameters where

can be a function of the diseased length, degree of stenosis andtapering ratio to account for tapered vessel; and

can be determined by summing distributed flow of each outlet on thebasis of the same scaling law as the resistance boundary condition (

). However, a new scaling law and hyperemic assumption can be adopted,and this feature vector may be associated with the measurement orsimulated value of the FFR at that point. Server systems 106 may alsotrain a linear SVM to predict the blood flow characteristics at thepoints from the feature vectors at the points; and save the results ofthe SVM as a digital representation (e.g., the memory or digital storage[e.g., hard drive, network drive] of a computational device such as acomputer, laptop, DSP, server, etc.).

In an exemplary production mode, servers systems 106 may, for a targetpatient, acquire in digital representation (e.g., the memory or digitalstorage (e.g., hard drive, network drive) of a computational device suchas a computer, laptop, DSP, server, etc.): (a) a patient-specific modelof the geometry for the patient's ascending aorta and coronary arterytree; and (b) a list of physiological and phenotypic parameters of thepatient obtained during training mode. In one embodiment, thepatient-specific model of the geometry for the patient's ascending aortaand coronary artery tree may be represented as a list of points in space(possibly with a list of neighbors for each point) in which the spacecan be mapped to spatial units between points (e.g., millimeters). Thismodel may be derived by performing a cardiac CT imaging of the patientin the end diastole phase of the cardiac cycle. This image then may besegmented manually or automatically to identify voxels belonging to theaorta and the lumen of the coronary arteries. Once the voxels areidentified, the geometric model can be derived (e.g., using marchingcubes). The process for generating the patient-specific model of thegeometry may be the same as in the training mode. For every point in thepatient-specific geometric model, the server systems 106 may create afeature vector for that point that consists of a numerical descriptionof the geometry at that point and estimates of physiological orphenotypic parameters of the patient. These features may be the same asthe quantities used in the training mode. The server systems 106 maythen use the saved results of the machine learning algorithm produced inthe training mode (e.g., feature weights) to produce estimates of theFFR at each point in the patient-specific geometric model. Theseestimates may be produced using the same linear SVM technique used inthe training mode. The server systems 106 may save the predicted FFRvalues for each point to a digital representation (e.g., the memory ordigital storage [e.g., hard drive, network drive] of a computationaldevice such as a computer, laptop, DSP, server, etc.).

In one embodiment, the above factors (i) thru (viii) (“Systolic anddiastolic blood pressures” thru “Characteristics of the coronary branchgeometry”) may be considered global features, which are applicable toall points within a given patient's geometric model. Also, items (ix)thru (xv) (“Feature Set I: Cross-sectional area feature” thru “FeatureSet VII: Simplified Physics feature”) may be considered features thatare local to specific points within a given patient's geometric model.In addition, features (i) thru (vi) may be considered variables withinthe function of boundary conditions, f(BCs), while features (vii) thru(xv) may be considered variables within the function of geometry,g(areaReductions), on that page. It will be appreciated that anycombination of those features, modified by any desired weighting scheme,may be incorporated into a machine learning algorithm executed accordingto the disclosed embodiments.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

1-22. (canceled)
 23. A method for determining individual-specific bloodflow characteristics, the method comprising: acquiring, by a processor,for each of a plurality of individuals, an individual-specific geometricmodel of at least part of a vascular system of each individual andvalues of a blood flow characteristic at one or more points of eachindividual's vascular system; training, by the processor, a machinelearning algorithm using individual-specific anatomic data derived fromone or more points of each individual-specific geometric model and thevalues of the blood flow characteristic at the one or more points ofeach individual's vascular system, wherein the training of the machinelearning algorithm generates learned associations by relating featuresidentified from each individual's individual-specific anatomic data andthe blood flow characteristic derived from each individual'sindividual-specific anatomic data at the one or more points of eachindividual-specific geometric model; acquiring, by the processor, for apatient different from the plurality of individuals, one or more imagesof patient-specific anatomic data of at least part of the patient'svascular system, wherein the patient-specific anatomic data includesdata corresponding to a lesion of interest in the patient's vascularsystem; generating, by the processor, a geometric model of an imageregion comprising the lesion of interest in the patient's vascularsystem, the image region corresponding to features of the patient'sanatomy predictive of the blood flow characteristic; and executing, bythe processor, the trained machine learning algorithm for at least apoint of the geometric model of the image region of the patient tonon-invasively determine values of the blood flow characteristic at oneor more points of the geometric model of the patient's vascular system,using the learned associations of features related between theindividual-specific anatomic data and the blood flow characteristic. 24.The method of claim 23, further comprising: acquiring, by the processor,for each of the plurality of individuals, one or more individualcharacteristics; and executing, by the processor, the machine learningalgorithm further based on the one or more individual characteristics.25. The method of claim 23, wherein the non-invasively determined bloodflow characteristics of the individuals include a measurement ofischemia, blood flow, or fractional flow reserve.
 26. The method ofclaim 23, further comprising: generating, by the processor, a set offeatures for each point of interest of the patient's vascular systemwhere a fractional flow reserve is desired; using, by the processor, aregression or machine learning technique to weight an impact of featureson the fractional flow reserve; and using, by the processor, theregression or machine learning technique to estimate the fractional flowreserve numerically, classify a vessel as ischemia positive or negative,or classify an individual as ischemia positive or negative.
 27. Themethod of claim 24, wherein the individual characteristics include oneor more of: heart rate, blood pressure, demographics including age orsex, medication, disease states, including diabetes, hypertension,vessel dominance, and prior myocardial infarction.
 28. The method ofclaim 23, further comprising: displaying or storing, by the processor,the blood flow characteristic in one or more of a media, includingimages, renderings, tables of values, or reports, or transferring theblood flow characteristic to a physician through other electronic orphysical delivery methods.
 29. The method of claim 23, furthercomprising displaying, by the processor, along with the blood flowcharacteristic a confidence level or a positive, negative, orinconclusive indication.
 30. The method of claim 23, further comprisingdetermining, by the processor, the blood flow characteristic based onone or more of analytical fluid dynamics equations and morphometryscaling laws.
 31. The method of claim 23, wherein theindividual-specific anatomic data includes one or more of: vessel size,vessel size at ostium, vessel size at distal branches, reference andminimum vessel size at plaque, distance from ostium to plaque, length ofplaque and length of minimum vessel size, myocardial volume, branchesproximal/distal to measurement location, branches proximal/distal toplaque, and measurement location.
 32. The method of claim 24, furthercomprising: compiling, by the processor, a library or database ofanatomic and individual characteristics along with fractional flowreserve (FFR), ischemia test results, previous simulation results, andimaging data.
 33. The method of claim 32, further comprising: refining,by the processor, the machine learning algorithm based on additionaldata added to the library or database.
 34. The method of claim 32,wherein the individual-specific anatomic data is obtained from one ormore of: medical image data, measurements, models, and segmentations.35. A system for determining individual-specific blood flowcharacteristics, the system comprising: a data storage device storinginstructions for estimating individual-specific blood flowcharacteristics; and a processor configured to execute the instructionsto perform a method including: acquiring, for each of a plurality ofindividuals, an individual-specific geometric model of at least part ofa vascular system of each individual and values of a blood flowcharacteristic at one or more points of each individual's vascularsystem; training a machine learning algorithm using individual-specificanatomic data derived from one or more points of eachindividual-specific geometric model and the values of the blood flowcharacteristic at the one or more points of each individual's vascularsystem, wherein the training of the machine learning algorithm generateslearned associations by relating features identified from eachindividual's individual-specific anatomic data and the blood flowcharacteristic derived from each individual's individual-specificanatomic data at the one or more points of each individual-specificgeometric model; acquiring, for a patient different from the pluralityof individuals, one or more images of patient-specific anatomic data ofat least part of the patient's vascular system, wherein thepatient-specific anatomic data includes data corresponding to a lesionof interest in the patient's vascular system; generating a geometricmodel of an image region comprising the lesion of interest in thepatient's vascular system, the image region corresponding to features ofthe patient's anatomy predictive of the blood flow characteristic; andexecuting the trained machine learning algorithm for at least a point ofthe geometric model of the image region of the patient to non-invasivelydetermine values of the blood flow characteristic at one or more pointsof the geometric model of the patient's vascular system, using thelearned associations of features related between the individual-specificanatomic data and the blood flow characteristic.
 36. The system of claim35, wherein the system is further configured for: acquiring, for each ofthe plurality of individuals, one or more individual characteristics;and executing the machine learning algorithm further based on the one ormore individual characteristics.
 37. The system of claim 35, wherein thenon-invasively determined blood flow characteristics include ameasurement of ischemia, blood flow, or fractional flow reserve.
 38. Thesystem of claim 35, wherein the processor is further configured for:generating a set of features for each point of interest of the patient'svascular system where a fractional flow reserve determination isdesired; using a regression or machine learning technique to weight animpact of features on the fractional flow reserve; and using theregression or machine learning technique to estimate the fractional flowreserve numerically, classify a vessel as ischemia positive or negative,or classify an individual as ischemia positive or negative.
 39. Thesystem of claim 36, wherein the individual characteristics include oneor more of: heart rate, blood pressure, demographics including age orsex, medication, disease states, including diabetes, hypertension,vessel dominance, and prior myocardial infarction (MI).
 40. The systemof claim 35, wherein the processor is further configured for: displayingor storing the blood flow characteristic in one or more of a media,including images, renderings, tables of values, or reports, ortransferring the blood flow characteristic to a physician through otherelectronic or physical delivery methods.
 41. The system of claim 35,wherein the processor is further configured for: displaying along withthe blood flow characteristic a confidence level or a positive,negative, or inconclusive indication.
 42. The system of claim 35,further comprising determining the blood flow characteristic based onone or more of analytical fluid dynamics equations and morphometryscaling laws.